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International Journal of Information and Communication Technology

Quantum computing-driven portfolio optimisation framework for the intelligent economy

Leihua Ai

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Portfolio optimisation in intelligent economic systems faces the challenge of portfolio explosion caused by expanding asset scales, making it difficult for classical solvers to obtain high-quality solutions within a limited timeframe.This paper proposes a hybrid quantum-classical optimisation framework that combines the global search capabilities of quantum annealing with the constraint expression capabilities of variational quantum algorithms, while reducing quantum resource requirements through asset graph partitioning.In experiments covering 20 to 120 real-market assets, the hybrid framework achieved an area under the curve of 0.716 for a portfolio of 120 assets, representing an approximately 29% improvement over classical genetic algorithms; it maintained an area under the curve of 0.921 even under a noise level of 10 -3 , demonstrating greater robustness than pure quantum annealing methods.The research confirms that the hybrid paradigm is a viable approach for solving large-scale combinatorial optimisation problems on current noisy quantum devices.

DOI
10.1504/ijict.2026.156257

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Portfolio optimization is a cornerstone of financial decision-making, traditionally relying on classical algorithms to balance risk and return. Recent advances in quantum computing offer a promising alternative, leveraging quantum algorithms to efficiently explore complex solution spaces and potentially outperform classical methods in high-dimensional settings. However, conventional quantum approaches typically assume a one-to-one correspondence between qubits and variables (e.g. financial assets), which severely limits the applicability of gate-based quantum systems due to current hardware constraints. As a result, only quantum annealing-like methods have been used in realistic scenarios. In this work, we show how a gate-based variational quantum algorithm can be applied to a real-world portfolio optimization problem by assigning multiple variables per qubit. Specifically, we address a problem involving over 250 variables, where the market graph representing a real stock market is iteratively partitioned into sub-portfolios of highly correlated assets. This approach enables improved scalability compared to traditional variational methods and opens new possibilities for quantum-enhanced financial applications.
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Portfolio optimization is a well-established problem in the field of finance and has recently gained attention in the context of quantum computing. The hardware limitations of quantum computers prevent the direct application of quantum algorithms to large-scale problems. We propose a novel two-stage approach that combines the strengths of both quantum annealing and gate-based quantum computing to efficiently solve large-scale portfolio optimization problems. Building upon the existing Large System Sampling Approximation (LSSA) approach, our work introduces a novel modification to the decomposition and aggregation steps of LSSA. We divide the problem into sub-problems of smaller sizes by solving the Maximum Independent Set (MIS) of the market graph and use a Parameterized Quantum Circuit (PQC) to aggregate the sub-problem solutions. We conduct extensive experiments on real-world data from the US stock market on up to 128 assets on simulators. Our results demonstrate that the proposed approach performs better with the same hardware resources. The outcomes of our research suggest that hybrid annealer-gate quantum computing can provide a practical and scalable solution to large-scale portfolio optimization problems, bridging the gap between theoretical advancements in quantum computing and real-world applications in finance.
Portfolio optimization is one of the most studied problems for demonstrating the near-term applications of quantum computing. However, large-scale problems cannot be solved on today's quantum hardware. In this work, we extend upon a study to use the best of both quantum annealing and gate-based quantum computing systems to enable solving large-scale optimization problems efficiently on the available hardware. The existing work uses a method called Large System Sampling Approximation (LSSA) that involves dividing the large problem into several smaller problems and then combining the multiple solutions to approximate the solution to the original problem. This paper introduces a novel technique to modify the sampling step of LSSA. We divide the portfolio optimization problem into sub-systems of smaller sizes by selecting a diverse set of assets that act as representatives of the entire market and capture the highest correlations among assets. We conduct tests on real-world stock data from the Indian stock market on up to 64 assets. Our experimentation shows that the hybrid approach performs at par with the traditional classical optimization methods with a good approximation ratio. We also demonstrate the effectiveness of our approach on a range of portfolio optimization problems of different sizes. We present the effects of different parameters on the proposed method and compare its performance with the earlier work. Our findings suggest that hybrid annealer-gate quantum computing can be a valuable tool for portfolio managers seeking to optimize their investment portfolios in the near future.
Portfolio optimization (PO) plays a central role in finance, encompassing both resource allocation and risk management. While numerous algorithms have been developed for continuous-variable PO, the inclusion of large, indivisible assets such as real estate transforms the task into a combinatorial optimization problem, which is NP-hard for classical computers. This study explores using quantum-classical hybrid algorithms for multi-discretized portfolio optimization, addressing the needs of real-world applications. We propose a flexible theoretical model that can solve PO problems with various types of discrete assets and constraints, which is compatible with both quantum and classical computing paradigms. Then, we use the D-Wave quantum processor and classical solver to determine optimal investment strategies. Experimental results indicate that the hybrid quantum algorithm outperforms both classical computing methods and quantum annealing algorithms in terms of return and risk management. This research enhances quantum computing applications in finance and provides insights into future algorithm design.